Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

doi:10.1007/s12204-017-1880-y

上海交通大学学报（英文版） ›› 2017, Vol. 22 ›› Issue (5): 624-632.doi: 10.1007/s12204-017-1880-y

Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

XU Gongguo¹* (徐公国), DUAN Xiusheng¹ (段修生), LÜ Hao² (吕豪)

(1. Department of Electronic and Optical Engineering, Ordnance Engineering College, Shijiazhuang 050003, China; 2. Department of Automation, University of Science and Technology of China, Hefei 230000, China)

出版日期:2017-09-30 发布日期:2017-09-30
通讯作者: XU Gongguo (徐公国) E-mail:xugguo@yeah.net

Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

XU Gongguo¹* (徐公国), DUAN Xiusheng¹ (段修生), LÜ Hao² (吕豪)

(1. Department of Electronic and Optical Engineering, Ordnance Engineering College, Shijiazhuang 050003, China; 2. Department of Automation, University of Science and Technology of China, Hefei 230000, China)

Online:2017-09-30 Published:2017-09-30
Contact: XU Gongguo (徐公国) E-mail:xugguo@yeah.net

摘要/Abstract

摘要： The paper aims at the problem of multi-targets threat degree being hard to be evaluated accurately in complex air defense battlefield environments. Combined with multi-sensors information fusion and interval-valued intuitionistic fuzzy sets (IVIFS) theories, the target priority determination is studied. The score and accuracy functions of IVIFS are improved with thinking about the hesitating information in order to increase the rationality. Then, the influence factors of target priority and the nonlinear relationship between the influence factors and target priority are analyzed. Next, the algorithms for calculating the factor weights and sensor weights are given. Based on the theory of IVIFS and technique for order preference by similarity to an ideal solution (TOPSIS), two methods of target priority determination based on the IVIFS and TOPSIS are proposed. At last, an application example verifies the effectiveness and flexibility of the proposed algorithms.

关键词: target priority, interval-valued intuitionistic fuzzy set (IVIFS), multi-sensors, information fusion, score and accuracy functions

Abstract: The paper aims at the problem of multi-targets threat degree being hard to be evaluated accurately in complex air defense battlefield environments. Combined with multi-sensors information fusion and interval-valued intuitionistic fuzzy sets (IVIFS) theories, the target priority determination is studied. The score and accuracy functions of IVIFS are improved with thinking about the hesitating information in order to increase the rationality. Then, the influence factors of target priority and the nonlinear relationship between the influence factors and target priority are analyzed. Next, the algorithms for calculating the factor weights and sensor weights are given. Based on the theory of IVIFS and technique for order preference by similarity to an ideal solution (TOPSIS), two methods of target priority determination based on the IVIFS and TOPSIS are proposed. At last, an application example verifies the effectiveness and flexibility of the proposed algorithms.

Key words: target priority, interval-valued intuitionistic fuzzy set (IVIFS), multi-sensors, information fusion, score and accuracy functions

中图分类号:

TP 391.7

XU Gongguo1* (徐公国), DUAN Xiusheng1 (段修生), LU Hao2 (吕豪). Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights[J]. 上海交通大学学报（英文版）, 2017, 22(5): 624-632.

XU Gongguo1* (徐公国), DUAN Xiusheng1 (段修生), LU Hao2 (吕豪). Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights[J]. Journal of shanghai Jiaotong University (Science), 2017, 22(5): 624-632.

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Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

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